Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations2242
Missing cells470
Missing cells (%)0.8%
Duplicate rows8
Duplicate rows (%)0.4%
Total size in memory2.4 MiB
Average record size in memory1.1 KiB

Variable types

DateTime1
Categorical7
Text13
Numeric4

Alerts

indicativo has constant value "1212E" Constant
nombre has constant value "ASTURIAS AEROPUERTO" Constant
provincia has constant value "ASTURIAS" Constant
altitud has constant value "127" Constant
Dataset has 8 (0.4%) duplicate rowsDuplicates
hrMax is highly overall correlated with hrMediaHigh correlation
hrMedia is highly overall correlated with hrMax and 1 other fieldsHigh correlation
hrMin is highly overall correlated with hrMediaHigh correlation
horaPresMax is highly imbalanced (69.7%) Imbalance
dir has 25 (1.1%) missing values Missing
racha has 25 (1.1%) missing values Missing
horaracha has 25 (1.1%) missing values Missing
presMax has 23 (1.0%) missing values Missing
horaPresMax has 23 (1.0%) missing values Missing
hrMax has 40 (1.8%) missing values Missing
horaHrMax has 40 (1.8%) missing values Missing
hrMin has 40 (1.8%) missing values Missing
horaHrMin has 40 (1.8%) missing values Missing

Reproduction

Analysis started2025-02-20 16:24:38.888221
Analysis finished2025-02-20 16:24:44.388338
Duration5.5 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

fecha
Date

Distinct2234
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Minimum2019-01-01 00:00:00
Maximum2025-02-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T17:24:44.669717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:44.905041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

indicativo
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.4 KiB
1212E
2242 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11210
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1212E
2nd row1212E
3rd row1212E
4th row1212E
5th row1212E

Common Values

ValueCountFrequency (%)
1212E 2242
100.0%

Length

2025-02-20T17:24:45.118750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T17:24:45.284624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1212e 2242
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4484
40.0%
2 4484
40.0%
E 2242
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4484
40.0%
2 4484
40.0%
E 2242
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4484
40.0%
2 4484
40.0%
E 2242
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4484
40.0%
2 4484
40.0%
E 2242
20.0%

nombre
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.0 KiB
ASTURIAS AEROPUERTO
2242 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters42598
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASTURIAS AEROPUERTO
2nd rowASTURIAS AEROPUERTO
3rd rowASTURIAS AEROPUERTO
4th rowASTURIAS AEROPUERTO
5th rowASTURIAS AEROPUERTO

Common Values

ValueCountFrequency (%)
ASTURIAS AEROPUERTO 2242
100.0%

Length

2025-02-20T17:24:45.436921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T17:24:45.583122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
asturias 2242
50.0%
aeropuerto 2242
50.0%

Most occurring characters

ValueCountFrequency (%)
A 6726
15.8%
R 6726
15.8%
S 4484
10.5%
T 4484
10.5%
U 4484
10.5%
E 4484
10.5%
O 4484
10.5%
I 2242
 
5.3%
2242
 
5.3%
P 2242
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6726
15.8%
R 6726
15.8%
S 4484
10.5%
T 4484
10.5%
U 4484
10.5%
E 4484
10.5%
O 4484
10.5%
I 2242
 
5.3%
2242
 
5.3%
P 2242
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6726
15.8%
R 6726
15.8%
S 4484
10.5%
T 4484
10.5%
U 4484
10.5%
E 4484
10.5%
O 4484
10.5%
I 2242
 
5.3%
2242
 
5.3%
P 2242
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6726
15.8%
R 6726
15.8%
S 4484
10.5%
T 4484
10.5%
U 4484
10.5%
E 4484
10.5%
O 4484
10.5%
I 2242
 
5.3%
2242
 
5.3%
P 2242
 
5.3%

provincia
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size124.9 KiB
ASTURIAS
2242 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters17936
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASTURIAS
2nd rowASTURIAS
3rd rowASTURIAS
4th rowASTURIAS
5th rowASTURIAS

Common Values

ValueCountFrequency (%)
ASTURIAS 2242
100.0%

Length

2025-02-20T17:24:45.763530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T17:24:45.929282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
asturias 2242
100.0%

Most occurring characters

ValueCountFrequency (%)
A 4484
25.0%
S 4484
25.0%
T 2242
12.5%
U 2242
12.5%
R 2242
12.5%
I 2242
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4484
25.0%
S 4484
25.0%
T 2242
12.5%
U 2242
12.5%
R 2242
12.5%
I 2242
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4484
25.0%
S 4484
25.0%
T 2242
12.5%
U 2242
12.5%
R 2242
12.5%
I 2242
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4484
25.0%
S 4484
25.0%
T 2242
12.5%
U 2242
12.5%
R 2242
12.5%
I 2242
12.5%

altitud
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.0 KiB
127
2242 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6726
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row127
2nd row127
3rd row127
4th row127
5th row127

Common Values

ValueCountFrequency (%)
127 2242
100.0%

Length

2025-02-20T17:24:46.067681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T17:24:46.181880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
127 2242
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2242
33.3%
2 2242
33.3%
7 2242
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2242
33.3%
2 2242
33.3%
7 2242
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2242
33.3%
2 2242
33.3%
7 2242
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2242
33.3%
2 2242
33.3%
7 2242
33.3%

tmed
Text

Distinct201
Distinct (%)9.0%
Missing20
Missing (%)0.9%
Memory size115.4 KiB
2025-02-20T17:24:46.579902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8280828
Min length3

Characters and Unicode

Total characters8506
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)1.3%

Sample

1st row5,7
2nd row7,9
3rd row6,1
4th row4,9
5th row6,1
ValueCountFrequency (%)
13,2 43
 
1.9%
13,6 43
 
1.9%
18,2 35
 
1.6%
12,8 35
 
1.6%
18,0 35
 
1.6%
12,4 34
 
1.5%
17,8 32
 
1.4%
16,8 31
 
1.4%
16,4 31
 
1.4%
12,0 29
 
1.3%
Other values (191) 1874
84.3%
2025-02-20T17:24:47.127146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2222
26.1%
1 1958
23.0%
2 748
 
8.8%
8 629
 
7.4%
0 574
 
6.7%
6 565
 
6.6%
4 482
 
5.7%
9 378
 
4.4%
7 362
 
4.3%
3 309
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2222
26.1%
1 1958
23.0%
2 748
 
8.8%
8 629
 
7.4%
0 574
 
6.7%
6 565
 
6.6%
4 482
 
5.7%
9 378
 
4.4%
7 362
 
4.3%
3 309
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2222
26.1%
1 1958
23.0%
2 748
 
8.8%
8 629
 
7.4%
0 574
 
6.7%
6 565
 
6.6%
4 482
 
5.7%
9 378
 
4.4%
7 362
 
4.3%
3 309
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2222
26.1%
1 1958
23.0%
2 748
 
8.8%
8 629
 
7.4%
0 574
 
6.7%
6 565
 
6.6%
4 482
 
5.7%
9 378
 
4.4%
7 362
 
4.3%
3 309
 
3.6%

prec
Text

Distinct240
Distinct (%)10.7%
Missing6
Missing (%)0.3%
Memory size114.0 KiB
2025-02-20T17:24:47.527188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0424866
Min length2

Characters and Unicode

Total characters6803
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)4.0%

Sample

1st row0,0
2nd row0,0
3rd row0,0
4th row0,0
5th row0,0
ValueCountFrequency (%)
0,0 983
44.0%
ip 132
 
5.9%
0,1 80
 
3.6%
0,2 56
 
2.5%
0,3 42
 
1.9%
0,4 37
 
1.7%
0,6 34
 
1.5%
0,9 26
 
1.2%
0,8 24
 
1.1%
0,7 24
 
1.1%
Other values (230) 798
35.7%
2025-02-20T17:24:48.112456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2441
35.9%
, 2104
30.9%
1 481
 
7.1%
2 328
 
4.8%
3 232
 
3.4%
4 196
 
2.9%
5 178
 
2.6%
6 154
 
2.3%
7 149
 
2.2%
8 143
 
2.1%
Other values (3) 397
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2441
35.9%
, 2104
30.9%
1 481
 
7.1%
2 328
 
4.8%
3 232
 
3.4%
4 196
 
2.9%
5 178
 
2.6%
6 154
 
2.3%
7 149
 
2.2%
8 143
 
2.1%
Other values (3) 397
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2441
35.9%
, 2104
30.9%
1 481
 
7.1%
2 328
 
4.8%
3 232
 
3.4%
4 196
 
2.9%
5 178
 
2.6%
6 154
 
2.3%
7 149
 
2.2%
8 143
 
2.1%
Other values (3) 397
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2441
35.9%
, 2104
30.9%
1 481
 
7.1%
2 328
 
4.8%
3 232
 
3.4%
4 196
 
2.9%
5 178
 
2.6%
6 154
 
2.3%
7 149
 
2.2%
8 143
 
2.1%
Other values (3) 397
 
5.8%

tmin
Text

Distinct199
Distinct (%)9.0%
Missing20
Missing (%)0.9%
Memory size114.8 KiB
2025-02-20T17:24:48.549655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5621062
Min length3

Characters and Unicode

Total characters7915
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.7%

Sample

1st row1,5
2nd row6,0
3rd row1,7
4th row-1,4
5th row0,0
ValueCountFrequency (%)
9,1 25
 
1.1%
10,6 25
 
1.1%
8,5 24
 
1.1%
8,3 24
 
1.1%
14,2 23
 
1.0%
15,9 23
 
1.0%
9,2 23
 
1.0%
5,8 22
 
1.0%
16,6 22
 
1.0%
9,6 22
 
1.0%
Other values (188) 1989
89.5%
2025-02-20T17:24:49.112740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2222
28.1%
1 1647
20.8%
6 496
 
6.3%
4 480
 
6.1%
5 476
 
6.0%
3 458
 
5.8%
7 443
 
5.6%
8 435
 
5.5%
2 429
 
5.4%
9 424
 
5.4%
Other values (2) 405
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2222
28.1%
1 1647
20.8%
6 496
 
6.3%
4 480
 
6.1%
5 476
 
6.0%
3 458
 
5.8%
7 443
 
5.6%
8 435
 
5.5%
2 429
 
5.4%
9 424
 
5.4%
Other values (2) 405
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2222
28.1%
1 1647
20.8%
6 496
 
6.3%
4 480
 
6.1%
5 476
 
6.0%
3 458
 
5.8%
7 443
 
5.6%
8 435
 
5.5%
2 429
 
5.4%
9 424
 
5.4%
Other values (2) 405
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2222
28.1%
1 1647
20.8%
6 496
 
6.3%
4 480
 
6.1%
5 476
 
6.0%
3 458
 
5.8%
7 443
 
5.6%
8 435
 
5.5%
2 429
 
5.4%
9 424
 
5.4%
Other values (2) 405
 
5.1%
Distinct703
Distinct (%)31.6%
Missing20
Missing (%)0.9%
Memory size118.1 KiB
2025-02-20T17:24:49.519689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0882088
Min length5

Characters and Unicode

Total characters11306
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique244 ?
Unique (%)11.0%

Sample

1st row04:58
2nd row23:49
3rd row23:55
4th row06:04
5th row01:44
ValueCountFrequency (%)
varias 196
 
8.8%
23:58 26
 
1.2%
23:57 21
 
0.9%
00:00 20
 
0.9%
23:59 19
 
0.9%
23:56 19
 
0.9%
23:30 18
 
0.8%
23:37 14
 
0.6%
23:54 13
 
0.6%
23:55 12
 
0.5%
Other values (693) 1864
83.9%
2025-02-20T17:24:50.109568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2226
19.7%
: 2026
17.9%
2 1204
10.6%
3 1132
10.0%
4 819
 
7.2%
5 777
 
6.9%
1 700
 
6.2%
6 416
 
3.7%
a 392
 
3.5%
7 352
 
3.1%
Other values (6) 1262
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2226
19.7%
: 2026
17.9%
2 1204
10.6%
3 1132
10.0%
4 819
 
7.2%
5 777
 
6.9%
1 700
 
6.2%
6 416
 
3.7%
a 392
 
3.5%
7 352
 
3.1%
Other values (6) 1262
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2226
19.7%
: 2026
17.9%
2 1204
10.6%
3 1132
10.0%
4 819
 
7.2%
5 777
 
6.9%
1 700
 
6.2%
6 416
 
3.7%
a 392
 
3.5%
7 352
 
3.1%
Other values (6) 1262
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2226
19.7%
: 2026
17.9%
2 1204
10.6%
3 1132
10.0%
4 819
 
7.2%
5 777
 
6.9%
1 700
 
6.2%
6 416
 
3.7%
a 392
 
3.5%
7 352
 
3.1%
Other values (6) 1262
11.2%

tmax
Text

Distinct221
Distinct (%)9.9%
Missing19
Missing (%)0.8%
Memory size115.7 KiB
2025-02-20T17:24:50.569837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9725596
Min length3

Characters and Unicode

Total characters8831
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)1.6%

Sample

1st row9,9
2nd row9,8
3rd row10,5
4th row11,2
5th row12,2
ValueCountFrequency (%)
21,3 29
 
1.3%
16,7 24
 
1.1%
19,3 24
 
1.1%
19,4 24
 
1.1%
16,3 24
 
1.1%
17,2 24
 
1.1%
21,8 24
 
1.1%
19,1 23
 
1.0%
20,0 22
 
1.0%
13,7 22
 
1.0%
Other values (211) 1983
89.2%
2025-02-20T17:24:51.112529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2223
25.2%
1 1904
21.6%
2 1228
13.9%
3 539
 
6.1%
0 469
 
5.3%
9 420
 
4.8%
6 418
 
4.7%
8 417
 
4.7%
7 416
 
4.7%
4 412
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2223
25.2%
1 1904
21.6%
2 1228
13.9%
3 539
 
6.1%
0 469
 
5.3%
9 420
 
4.8%
6 418
 
4.7%
8 417
 
4.7%
7 416
 
4.7%
4 412
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2223
25.2%
1 1904
21.6%
2 1228
13.9%
3 539
 
6.1%
0 469
 
5.3%
9 420
 
4.8%
6 418
 
4.7%
8 417
 
4.7%
7 416
 
4.7%
4 412
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2223
25.2%
1 1904
21.6%
2 1228
13.9%
3 539
 
6.1%
0 469
 
5.3%
9 420
 
4.8%
6 418
 
4.7%
8 417
 
4.7%
7 416
 
4.7%
4 412
 
4.7%
Distinct591
Distinct (%)26.6%
Missing19
Missing (%)0.8%
Memory size118.1 KiB
2025-02-20T17:24:51.543008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0530814
Min length5

Characters and Unicode

Total characters11233
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)9.8%

Sample

1st row13:30
2nd rowVarias
3rd row13:40
4th row14:12
5th row14:33
ValueCountFrequency (%)
varias 118
 
5.3%
13:23 17
 
0.8%
13:12 15
 
0.7%
14:10 14
 
0.6%
14:29 13
 
0.6%
13:13 13
 
0.6%
13:27 12
 
0.5%
13:10 12
 
0.5%
13:49 12
 
0.5%
12:22 11
 
0.5%
Other values (581) 1986
89.3%
2025-02-20T17:24:52.161803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2685
23.9%
: 2105
18.7%
2 1051
 
9.4%
3 1050
 
9.3%
0 980
 
8.7%
4 891
 
7.9%
5 749
 
6.7%
9 285
 
2.5%
6 256
 
2.3%
7 240
 
2.1%
Other values (6) 941
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2685
23.9%
: 2105
18.7%
2 1051
 
9.4%
3 1050
 
9.3%
0 980
 
8.7%
4 891
 
7.9%
5 749
 
6.7%
9 285
 
2.5%
6 256
 
2.3%
7 240
 
2.1%
Other values (6) 941
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2685
23.9%
: 2105
18.7%
2 1051
 
9.4%
3 1050
 
9.3%
0 980
 
8.7%
4 891
 
7.9%
5 749
 
6.7%
9 285
 
2.5%
6 256
 
2.3%
7 240
 
2.1%
Other values (6) 941
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2685
23.9%
: 2105
18.7%
2 1051
 
9.4%
3 1050
 
9.3%
0 980
 
8.7%
4 891
 
7.9%
5 749
 
6.7%
9 285
 
2.5%
6 256
 
2.3%
7 240
 
2.1%
Other values (6) 941
 
8.4%

dir
Real number (ℝ)

Missing 

Distinct37
Distinct (%)1.7%
Missing25
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean43.396933
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2025-02-20T17:24:52.325350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q119
median27
Q399
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)80

Descriptive statistics

Standard deviation36.688935
Coefficient of variation (CV)0.84542691
Kurtosis-1.2206261
Mean43.396933
Median Absolute Deviation (MAD)9
Skewness0.77444061
Sum96211
Variance1346.0779
MonotonicityNot monotonic
2025-02-20T17:24:52.517608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
99 655
29.2%
27 180
 
8.0%
28 133
 
5.9%
20 97
 
4.3%
19 96
 
4.3%
26 95
 
4.2%
22 92
 
4.1%
29 87
 
3.9%
8 70
 
3.1%
24 64
 
2.9%
Other values (27) 648
28.9%
ValueCountFrequency (%)
1 7
 
0.3%
2 7
 
0.3%
3 11
 
0.5%
4 22
 
1.0%
5 34
1.5%
6 60
2.7%
7 38
1.7%
8 70
3.1%
9 48
2.1%
10 49
2.2%
ValueCountFrequency (%)
99 655
29.2%
36 6
 
0.3%
35 6
 
0.3%
34 3
 
0.1%
33 7
 
0.3%
32 16
 
0.7%
31 27
 
1.2%
30 23
 
1.0%
29 87
 
3.9%
28 133
 
5.9%

velmedia
Categorical

Distinct43
Distinct (%)1.9%
Missing15
Missing (%)0.7%
Memory size114.1 KiB
2,8
232 
2,5
224 
3,1
191 
2,2
184 
3,3
179 
Other values (38)
1217 

Length

Max length4
Median length3
Mean length3.0094297
Min length3

Characters and Unicode

Total characters6702
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st row4,7
2nd row4,2
3rd row5,3
4th row3,9
5th row3,3

Common Values

ValueCountFrequency (%)
2,8 232
 
10.3%
2,5 224
 
10.0%
3,1 191
 
8.5%
2,2 184
 
8.2%
3,3 179
 
8.0%
3,6 152
 
6.8%
1,9 143
 
6.4%
3,9 127
 
5.7%
4,2 106
 
4.7%
4,4 95
 
4.2%
Other values (33) 594
26.5%

Length

2025-02-20T17:24:52.652287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,8 232
 
10.4%
2,5 224
 
10.1%
3,1 191
 
8.6%
2,2 184
 
8.3%
3,3 179
 
8.0%
3,6 152
 
6.8%
1,9 143
 
6.4%
3,9 127
 
5.7%
4,2 106
 
4.8%
4,4 95
 
4.3%
Other values (33) 594
26.7%

Most occurring characters

ValueCountFrequency (%)
, 2227
33.2%
2 951
14.2%
3 883
 
13.2%
1 553
 
8.3%
4 439
 
6.6%
5 414
 
6.2%
8 326
 
4.9%
9 317
 
4.7%
6 308
 
4.6%
7 202
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2227
33.2%
2 951
14.2%
3 883
 
13.2%
1 553
 
8.3%
4 439
 
6.6%
5 414
 
6.2%
8 326
 
4.9%
9 317
 
4.7%
6 308
 
4.6%
7 202
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2227
33.2%
2 951
14.2%
3 883
 
13.2%
1 553
 
8.3%
4 439
 
6.6%
5 414
 
6.2%
8 326
 
4.9%
9 317
 
4.7%
6 308
 
4.6%
7 202
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2227
33.2%
2 951
14.2%
3 883
 
13.2%
1 553
 
8.3%
4 439
 
6.6%
5 414
 
6.2%
8 326
 
4.9%
9 317
 
4.7%
6 308
 
4.6%
7 202
 
3.0%

racha
Text

Missing 

Distinct58
Distinct (%)2.6%
Missing25
Missing (%)1.1%
Memory size114.4 KiB
2025-02-20T17:24:52.913669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.435724
Min length3

Characters and Unicode

Total characters7617
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.4%

Sample

1st row8,9
2nd row12,8
3rd row10,8
4th row8,3
5th row7,2
ValueCountFrequency (%)
8,3 189
 
8.5%
8,9 186
 
8.4%
7,8 180
 
8.1%
9,2 148
 
6.7%
7,2 134
 
6.0%
9,7 130
 
5.9%
10,3 123
 
5.5%
6,7 100
 
4.5%
6,1 95
 
4.3%
10,8 90
 
4.1%
Other values (48) 842
38.0%
2025-02-20T17:24:53.396445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2217
29.1%
1 1151
15.1%
8 831
 
10.9%
7 682
 
9.0%
2 566
 
7.4%
9 563
 
7.4%
3 534
 
7.0%
0 331
 
4.3%
6 324
 
4.3%
5 228
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2217
29.1%
1 1151
15.1%
8 831
 
10.9%
7 682
 
9.0%
2 566
 
7.4%
9 563
 
7.4%
3 534
 
7.0%
0 331
 
4.3%
6 324
 
4.3%
5 228
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2217
29.1%
1 1151
15.1%
8 831
 
10.9%
7 682
 
9.0%
2 566
 
7.4%
9 563
 
7.4%
3 534
 
7.0%
0 331
 
4.3%
6 324
 
4.3%
5 228
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2217
29.1%
1 1151
15.1%
8 831
 
10.9%
7 682
 
9.0%
2 566
 
7.4%
9 563
 
7.4%
3 534
 
7.0%
0 331
 
4.3%
6 324
 
4.3%
5 228
 
3.0%

horaracha
Text

Missing 

Distinct894
Distinct (%)40.3%
Missing25
Missing (%)1.1%
Memory size118.5 KiB
2025-02-20T17:24:53.790066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.3179973
Min length5

Characters and Unicode

Total characters11790
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique484 ?
Unique (%)21.8%

Sample

1st row10:44
2nd row19:18
3rd rowVarias
4th rowVarias
5th rowVarias
ValueCountFrequency (%)
varias 705
31.8%
14:14 6
 
0.3%
04:47 6
 
0.3%
04:48 6
 
0.3%
09:49 6
 
0.3%
04:50 5
 
0.2%
09:05 5
 
0.2%
23:51 5
 
0.2%
14:55 5
 
0.2%
07:26 5
 
0.2%
Other values (884) 1463
66.0%
2025-02-20T17:24:54.382367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 1512
12.8%
a 1410
12.0%
1 1260
10.7%
0 1241
10.5%
i 705
 
6.0%
r 705
 
6.0%
s 705
 
6.0%
V 705
 
6.0%
2 675
 
5.7%
3 591
 
5.0%
Other values (6) 2281
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 1512
12.8%
a 1410
12.0%
1 1260
10.7%
0 1241
10.5%
i 705
 
6.0%
r 705
 
6.0%
s 705
 
6.0%
V 705
 
6.0%
2 675
 
5.7%
3 591
 
5.0%
Other values (6) 2281
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 1512
12.8%
a 1410
12.0%
1 1260
10.7%
0 1241
10.5%
i 705
 
6.0%
r 705
 
6.0%
s 705
 
6.0%
V 705
 
6.0%
2 675
 
5.7%
3 591
 
5.0%
Other values (6) 2281
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 1512
12.8%
a 1410
12.0%
1 1260
10.7%
0 1241
10.5%
i 705
 
6.0%
r 705
 
6.0%
s 705
 
6.0%
V 705
 
6.0%
2 675
 
5.7%
3 591
 
5.0%
Other values (6) 2281
19.3%

sol
Text

Distinct146
Distinct (%)6.6%
Missing13
Missing (%)0.6%
Memory size114.0 KiB
2025-02-20T17:24:54.787783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1422162
Min length3

Characters and Unicode

Total characters7004
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st row2,1
2nd row0,0
3rd row7,9
4th row8,6
5th row8,6
ValueCountFrequency (%)
0,0 205
 
9.2%
0,2 45
 
2.0%
0,3 39
 
1.7%
0,5 39
 
1.7%
0,1 37
 
1.7%
0,4 35
 
1.6%
2,4 29
 
1.3%
8,5 26
 
1.2%
0,8 25
 
1.1%
2,1 24
 
1.1%
Other values (136) 1725
77.4%
2025-02-20T17:24:55.312553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2229
31.8%
0 981
14.0%
1 841
 
12.0%
2 469
 
6.7%
4 392
 
5.6%
3 387
 
5.5%
5 385
 
5.5%
8 358
 
5.1%
6 349
 
5.0%
7 312
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2229
31.8%
0 981
14.0%
1 841
 
12.0%
2 469
 
6.7%
4 392
 
5.6%
3 387
 
5.5%
5 385
 
5.5%
8 358
 
5.1%
6 349
 
5.0%
7 312
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2229
31.8%
0 981
14.0%
1 841
 
12.0%
2 469
 
6.7%
4 392
 
5.6%
3 387
 
5.5%
5 385
 
5.5%
8 358
 
5.1%
6 349
 
5.0%
7 312
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2229
31.8%
0 981
14.0%
1 841
 
12.0%
2 469
 
6.7%
4 392
 
5.6%
3 387
 
5.5%
5 385
 
5.5%
8 358
 
5.1%
6 349
 
5.0%
7 312
 
4.5%

presMax
Text

Missing 

Distinct351
Distinct (%)15.8%
Missing23
Missing (%)1.0%
Memory size119.7 KiB
2025-02-20T17:24:55.821918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.8337089
Min length5

Characters and Unicode

Total characters12945
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)3.1%

Sample

1st row1020,2
2nd row1021,0
3rd row1017,5
4th row1019,4
5th row1021,6
ValueCountFrequency (%)
1005,7 25
 
1.1%
1007,9 23
 
1.0%
1007,6 22
 
1.0%
1004,0 21
 
0.9%
1007,3 20
 
0.9%
1004,4 20
 
0.9%
1006,4 19
 
0.9%
1007,8 19
 
0.9%
1003,7 19
 
0.9%
1001,0 19
 
0.9%
Other values (341) 2012
90.7%
2025-02-20T17:24:56.409037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3594
27.8%
1 2757
21.3%
, 2219
17.1%
9 1140
 
8.8%
7 515
 
4.0%
6 470
 
3.6%
4 469
 
3.6%
2 467
 
3.6%
5 453
 
3.5%
8 440
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3594
27.8%
1 2757
21.3%
, 2219
17.1%
9 1140
 
8.8%
7 515
 
4.0%
6 470
 
3.6%
4 469
 
3.6%
2 467
 
3.6%
5 453
 
3.5%
8 440
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3594
27.8%
1 2757
21.3%
, 2219
17.1%
9 1140
 
8.8%
7 515
 
4.0%
6 470
 
3.6%
4 469
 
3.6%
2 467
 
3.6%
5 453
 
3.5%
8 440
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3594
27.8%
1 2757
21.3%
, 2219
17.1%
9 1140
 
8.8%
7 515
 
4.0%
6 470
 
3.6%
4 469
 
3.6%
2 467
 
3.6%
5 453
 
3.5%
8 440
 
3.4%

horaPresMax
Categorical

Imbalance  Missing 

Distinct20
Distinct (%)0.9%
Missing23
Missing (%)1.0%
Memory size117.3 KiB
Varias
1377 
00
740 
01
 
28
24
 
23
23
 
10
Other values (15)
 
41

Length

Max length6
Median length6
Mean length4.4821992
Min length2

Characters and Unicode

Total characters9946
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.3%

Sample

1st rowVarias
2nd rowVarias
3rd row00
4th rowVarias
5th rowVarias

Common Values

ValueCountFrequency (%)
Varias 1377
61.4%
00 740
33.0%
01 28
 
1.2%
24 23
 
1.0%
23 10
 
0.4%
21 9
 
0.4%
22 6
 
0.3%
11 6
 
0.3%
10 5
 
0.2%
06 2
 
0.1%
Other values (10) 13
 
0.6%
(Missing) 23
 
1.0%

Length

2025-02-20T17:24:56.571067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
varias 1377
62.1%
00 740
33.3%
01 28
 
1.3%
24 23
 
1.0%
23 10
 
0.5%
21 9
 
0.4%
22 6
 
0.3%
11 6
 
0.3%
10 5
 
0.2%
06 2
 
0.1%
Other values (10) 13
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 2754
27.7%
0 1522
15.3%
V 1377
13.8%
r 1377
13.8%
i 1377
13.8%
s 1377
13.8%
1 60
 
0.6%
2 59
 
0.6%
4 24
 
0.2%
3 13
 
0.1%
Other values (4) 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2754
27.7%
0 1522
15.3%
V 1377
13.8%
r 1377
13.8%
i 1377
13.8%
s 1377
13.8%
1 60
 
0.6%
2 59
 
0.6%
4 24
 
0.2%
3 13
 
0.1%
Other values (4) 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2754
27.7%
0 1522
15.3%
V 1377
13.8%
r 1377
13.8%
i 1377
13.8%
s 1377
13.8%
1 60
 
0.6%
2 59
 
0.6%
4 24
 
0.2%
3 13
 
0.1%
Other values (4) 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2754
27.7%
0 1522
15.3%
V 1377
13.8%
r 1377
13.8%
i 1377
13.8%
s 1377
13.8%
1 60
 
0.6%
2 59
 
0.6%
4 24
 
0.2%
3 13
 
0.1%
Other values (4) 6
 
0.1%
Distinct408
Distinct (%)18.4%
Missing22
Missing (%)1.0%
Memory size119.1 KiB
2025-02-20T17:24:56.992953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5788288
Min length5

Characters and Unicode

Total characters12385
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)4.3%

Sample

1st row1016,8
2nd row1017,5
3rd row1014,5
4th row1015,1
5th row1019,0
ValueCountFrequency (%)
1002,5 22
 
1.0%
998,3 22
 
1.0%
1001,4 20
 
0.9%
1001,1 18
 
0.8%
1007,2 18
 
0.8%
998,0 18
 
0.8%
1001,3 17
 
0.8%
1000,1 17
 
0.8%
1000,9 17
 
0.8%
1005,2 17
 
0.8%
Other values (398) 2034
91.6%
2025-02-20T17:24:57.520679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2775
22.4%
, 2220
17.9%
9 2076
16.8%
1 1987
16.0%
8 587
 
4.7%
7 506
 
4.1%
3 457
 
3.7%
6 451
 
3.6%
5 447
 
3.6%
4 440
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2775
22.4%
, 2220
17.9%
9 2076
16.8%
1 1987
16.0%
8 587
 
4.7%
7 506
 
4.1%
3 457
 
3.7%
6 451
 
3.6%
5 447
 
3.6%
4 440
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2775
22.4%
, 2220
17.9%
9 2076
16.8%
1 1987
16.0%
8 587
 
4.7%
7 506
 
4.1%
3 457
 
3.7%
6 451
 
3.6%
5 447
 
3.6%
4 440
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2775
22.4%
, 2220
17.9%
9 2076
16.8%
1 1987
16.0%
8 587
 
4.7%
7 506
 
4.1%
3 457
 
3.7%
6 451
 
3.6%
5 447
 
3.6%
4 440
 
3.6%

horaPresMin
Categorical

Distinct26
Distinct (%)1.2%
Missing22
Missing (%)1.0%
Memory size112.0 KiB
24
461 
00
235 
04
220 
03
171 
02
125 
Other values (21)
1008 

Length

Max length6
Median length2
Mean length2.0558559
Min length2

Characters and Unicode

Total characters4564
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row24
3rd row14
4th row05
5th row05

Common Values

ValueCountFrequency (%)
24 461
20.6%
00 235
10.5%
04 220
9.8%
03 171
 
7.6%
02 125
 
5.6%
16 117
 
5.2%
15 111
 
5.0%
05 109
 
4.9%
01 109
 
4.9%
17 91
 
4.1%
Other values (16) 471
21.0%

Length

2025-02-20T17:24:57.683873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
24 461
20.8%
00 235
10.6%
04 220
9.9%
03 171
 
7.7%
02 125
 
5.6%
16 117
 
5.3%
15 111
 
5.0%
05 109
 
4.9%
01 109
 
4.9%
17 91
 
4.1%
Other values (16) 471
21.2%

Most occurring characters

ValueCountFrequency (%)
0 1324
29.0%
4 766
16.8%
2 722
15.8%
1 698
15.3%
3 246
 
5.4%
5 220
 
4.8%
6 158
 
3.5%
7 121
 
2.7%
8 84
 
1.8%
a 62
 
1.4%
Other values (5) 163
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1324
29.0%
4 766
16.8%
2 722
15.8%
1 698
15.3%
3 246
 
5.4%
5 220
 
4.8%
6 158
 
3.5%
7 121
 
2.7%
8 84
 
1.8%
a 62
 
1.4%
Other values (5) 163
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1324
29.0%
4 766
16.8%
2 722
15.8%
1 698
15.3%
3 246
 
5.4%
5 220
 
4.8%
6 158
 
3.5%
7 121
 
2.7%
8 84
 
1.8%
a 62
 
1.4%
Other values (5) 163
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1324
29.0%
4 766
16.8%
2 722
15.8%
1 698
15.3%
3 246
 
5.4%
5 220
 
4.8%
6 158
 
3.5%
7 121
 
2.7%
8 84
 
1.8%
a 62
 
1.4%
Other values (5) 163
 
3.6%

hrMedia
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)2.7%
Missing13
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean78.87214
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2025-02-20T17:24:57.825513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile60
Q173
median80
Q386
95-th percentile94
Maximum100
Range70
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.349038
Coefficient of variation (CV)0.13121285
Kurtosis0.69725759
Mean78.87214
Median Absolute Deviation (MAD)7
Skewness-0.67889594
Sum175806
Variance107.10259
MonotonicityNot monotonic
2025-02-20T17:24:57.981869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 106
 
4.7%
83 93
 
4.1%
81 91
 
4.1%
77 88
 
3.9%
85 86
 
3.8%
80 86
 
3.8%
87 85
 
3.8%
78 85
 
3.8%
79 84
 
3.7%
88 82
 
3.7%
Other values (51) 1343
59.9%
ValueCountFrequency (%)
30 1
 
< 0.1%
38 1
 
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
43 4
0.2%
45 3
0.1%
46 3
0.1%
47 3
0.1%
48 5
0.2%
49 4
0.2%
ValueCountFrequency (%)
100 14
 
0.6%
99 5
 
0.2%
98 13
 
0.6%
97 16
 
0.7%
96 23
1.0%
95 24
1.1%
94 27
1.2%
93 31
1.4%
92 49
2.2%
91 54
2.4%

hrMax
Real number (ℝ)

High correlation  Missing 

Distinct39
Distinct (%)1.8%
Missing40
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean94.449591
Minimum60
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2025-02-20T17:24:58.159002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile84
Q193
median96
Q398
95-th percentile100
Maximum100
Range40
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.7098122
Coefficient of variation (CV)0.060453541
Kurtosis7.1580071
Mean94.449591
Median Absolute Deviation (MAD)3
Skewness-2.2254065
Sum207978
Variance32.601956
MonotonicityNot monotonic
2025-02-20T17:24:58.339898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
100 386
17.2%
96 264
11.8%
97 260
11.6%
95 210
9.4%
98 193
8.6%
94 154
 
6.9%
93 114
 
5.1%
92 103
 
4.6%
99 79
 
3.5%
90 75
 
3.3%
Other values (29) 364
16.2%
ValueCountFrequency (%)
60 1
 
< 0.1%
61 1
 
< 0.1%
62 3
0.1%
63 2
0.1%
64 2
0.1%
65 2
0.1%
66 3
0.1%
68 1
 
< 0.1%
69 2
0.1%
70 3
0.1%
ValueCountFrequency (%)
100 386
17.2%
99 79
 
3.5%
98 193
8.6%
97 260
11.6%
96 264
11.8%
95 210
9.4%
94 154
 
6.9%
93 114
 
5.1%
92 103
 
4.6%
91 72
 
3.2%

horaHrMax
Text

Missing 

Distinct436
Distinct (%)19.8%
Missing40
Missing (%)1.8%
Memory size118.9 KiB
2025-02-20T17:24:58.789556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6684832
Min length5

Characters and Unicode

Total characters12482
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique309 ?
Unique (%)14.0%

Sample

1st rowVarias
2nd rowVarias
3rd rowVarias
4th row00:51
5th rowVarias
ValueCountFrequency (%)
varias 1472
66.8%
00:00 43
 
2.0%
00:30 26
 
1.2%
23:59 13
 
0.6%
05:30 9
 
0.4%
07:00 9
 
0.4%
23:30 8
 
0.4%
00:01 7
 
0.3%
03:30 7
 
0.3%
00:02 6
 
0.3%
Other values (426) 602
27.3%
2025-02-20T17:24:59.419889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2944
23.6%
V 1472
11.8%
r 1472
11.8%
i 1472
11.8%
s 1472
11.8%
0 1033
 
8.3%
: 730
 
5.8%
3 398
 
3.2%
1 352
 
2.8%
2 321
 
2.6%
Other values (6) 816
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2944
23.6%
V 1472
11.8%
r 1472
11.8%
i 1472
11.8%
s 1472
11.8%
0 1033
 
8.3%
: 730
 
5.8%
3 398
 
3.2%
1 352
 
2.8%
2 321
 
2.6%
Other values (6) 816
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2944
23.6%
V 1472
11.8%
r 1472
11.8%
i 1472
11.8%
s 1472
11.8%
0 1033
 
8.3%
: 730
 
5.8%
3 398
 
3.2%
1 352
 
2.8%
2 321
 
2.6%
Other values (6) 816
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2944
23.6%
V 1472
11.8%
r 1472
11.8%
i 1472
11.8%
s 1472
11.8%
0 1033
 
8.3%
: 730
 
5.8%
3 398
 
3.2%
1 352
 
2.8%
2 321
 
2.6%
Other values (6) 816
 
6.5%

hrMin
Real number (ℝ)

High correlation  Missing 

Distinct78
Distinct (%)3.5%
Missing40
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean61.995459
Minimum19
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2025-02-20T17:24:59.610273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile40
Q154
median62
Q371
95-th percentile82
Maximum100
Range81
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.934532
Coefficient of variation (CV)0.20863676
Kurtosis-0.099834459
Mean61.995459
Median Absolute Deviation (MAD)9
Skewness-0.17482671
Sum136514
Variance167.30211
MonotonicityNot monotonic
2025-02-20T17:24:59.918459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 82
 
3.7%
62 79
 
3.5%
64 78
 
3.5%
70 76
 
3.4%
60 71
 
3.2%
58 70
 
3.1%
72 69
 
3.1%
54 69
 
3.1%
68 68
 
3.0%
61 63
 
2.8%
Other values (68) 1477
65.9%
ValueCountFrequency (%)
19 1
 
< 0.1%
21 1
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
26 2
 
0.1%
27 4
0.2%
28 3
0.1%
29 6
0.3%
30 2
 
0.1%
31 3
0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 3
0.1%
94 3
0.1%
93 6
0.3%
92 6
0.3%
91 2
 
0.1%
90 6
0.3%

horaHrMin
Text

Missing 

Distinct798
Distinct (%)36.2%
Missing40
Missing (%)1.8%
Memory size117.5 KiB
2025-02-20T17:25:00.393366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0140781
Min length5

Characters and Unicode

Total characters11041
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique318 ?
Unique (%)14.4%

Sample

1st row22:31
2nd row19:20
3rd row12:55
4th row13:41
5th row11:52
ValueCountFrequency (%)
varias 31
 
1.4%
00:00 16
 
0.7%
13:03 13
 
0.6%
13:24 12
 
0.5%
13:07 10
 
0.5%
12:11 10
 
0.5%
11:34 10
 
0.5%
13:17 10
 
0.5%
15:02 9
 
0.4%
14:48 9
 
0.4%
Other values (788) 2072
94.1%
2025-02-20T17:25:00.978875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2504
22.7%
: 2171
19.7%
0 1298
11.8%
2 1025
9.3%
3 974
 
8.8%
4 822
 
7.4%
5 777
 
7.0%
9 367
 
3.3%
6 324
 
2.9%
8 313
 
2.8%
Other values (6) 466
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2504
22.7%
: 2171
19.7%
0 1298
11.8%
2 1025
9.3%
3 974
 
8.8%
4 822
 
7.4%
5 777
 
7.0%
9 367
 
3.3%
6 324
 
2.9%
8 313
 
2.8%
Other values (6) 466
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2504
22.7%
: 2171
19.7%
0 1298
11.8%
2 1025
9.3%
3 974
 
8.8%
4 822
 
7.4%
5 777
 
7.0%
9 367
 
3.3%
6 324
 
2.9%
8 313
 
2.8%
Other values (6) 466
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2504
22.7%
: 2171
19.7%
0 1298
11.8%
2 1025
9.3%
3 974
 
8.8%
4 822
 
7.4%
5 777
 
7.0%
9 367
 
3.3%
6 324
 
2.9%
8 313
 
2.8%
Other values (6) 466
 
4.2%

Interactions

2025-02-20T17:24:42.088995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:39.939461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:40.610875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:41.421074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:42.239249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:40.089953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:40.768356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:41.572386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:42.405281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:40.280904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:41.051379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:41.737619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:42.578262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:40.455842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:41.236059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-20T17:24:41.914163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-20T17:25:01.114635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
dirhoraPresMaxhoraPresMinhrMaxhrMediahrMinvelmedia
dir1.0000.0590.1150.0720.0200.0110.143
horaPresMax0.0591.0000.1550.0230.1060.0000.149
horaPresMin0.1150.1551.0000.0630.0650.0630.105
hrMax0.0720.0230.0631.0000.6040.4110.000
hrMedia0.0200.1060.0650.6041.0000.8420.122
hrMin0.0110.0000.0630.4110.8421.0000.122
velmedia0.1430.1490.1050.0000.1220.1221.000

Missing values

2025-02-20T17:24:42.866474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T17:24:43.397742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-20T17:24:43.934971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachasolpresMaxhoraPresMaxpresMinhoraPresMinhrMediahrMaxhoraHrMaxhrMinhoraHrMin
02019-01-011212EASTURIAS AEROPUERTOASTURIAS1275,70,01,504:589,913:3013.04,78,910:442,11020,2Varias1016,80190.098.0Varias80.022:31
12019-01-021212EASTURIAS AEROPUERTOASTURIAS1277,90,06,023:499,8Varias99.04,212,819:180,01021,0Varias1017,52476.086.0Varias68.019:20
22019-01-031212EASTURIAS AEROPUERTOASTURIAS1276,10,01,723:5510,513:4099.05,310,8Varias7,91017,5001014,51477.095.0Varias63.012:55
32019-01-041212EASTURIAS AEROPUERTOASTURIAS1274,90,0-1,406:0411,214:1299.03,98,3Varias8,61019,4Varias1015,10575.096.000:5151.013:41
42019-01-051212EASTURIAS AEROPUERTOASTURIAS1276,10,00,001:4412,214:3399.03,37,2Varias8,61021,6Varias1019,00574.093.0Varias46.011:52
52019-01-061212EASTURIAS AEROPUERTOASTURIAS1275,00,0-1,008:1011,013:3811.03,99,216:348,31021,6Varias1019,61481.096.0Varias66.013:08
62019-01-071212EASTURIAS AEROPUERTOASTURIAS1274,90,0-0,202:0410,013:1610.05,011,4Varias8,31022,0Varias1019,61476.097.0Varias65.012:55
72019-01-081212EASTURIAS AEROPUERTOASTURIAS1276,20,00,906:5311,613:0099.02,87,8Varias5,81020,5Varias1017,32479.096.0Varias62.012:20
82019-01-091212EASTURIAS AEROPUERTOASTURIAS1277,60,55,123:3010,215:3021.02,59,201:590,01017,3001011,72488.097.0Varias77.017:59
92019-01-101212EASTURIAS AEROPUERTOASTURIAS1276,00,02,222:129,914:288.04,210,814:412,41014,8Varias1010,70577.096.000:2659.015:05
fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachasolpresMaxhoraPresMaxpresMinhoraPresMinhrMediahrMaxhoraHrMaxhrMinhoraHrMin
22322025-02-021212EASTURIAS AEROPUERTOASTURIAS1278,932,76,123:0511,713:4499.04,415,3Varias1,01010,6Varias1003,40687.098.008:2262.013:54
22332025-02-031212EASTURIAS AEROPUERTOASTURIAS1279,40,46,100:1812,612:1020.02,87,800:356,51012,1Varias1009,71687.097.0Varias71.014:05
22342025-02-041212EASTURIAS AEROPUERTOASTURIAS12710,10,06,107:0614,113:1599.02,58,3Varias6,61018,6Varias1009,50473.093.001:3351.013:02
22352025-02-051212EASTURIAS AEROPUERTOASTURIAS1279,60,05,7Varias13,413:0099.03,37,2Varias6,91021,0Varias1018,12477.096.0Varias61.011:43
22362025-02-061212EASTURIAS AEROPUERTOASTURIAS1277,913,53,506:3012,314:2210.03,68,911:425,31018,1001005,82484.097.0Varias67.014:27
22372025-02-071212EASTURIAS AEROPUERTOASTURIAS1276,99,95,223:308,600:2899.02,59,2Varias0,01005,800997,20888.097.0Varias66.018:20
22382025-02-081212EASTURIAS AEROPUERTOASTURIAS1277,21,73,305:3811,112:0320.03,19,704:583,61007,8Varias1004,90265.093.0Varias48.015:20
22392025-02-091212EASTURIAS AEROPUERTOASTURIAS12712,02,48,800:0015,310:5999.02,58,3Varias0,01007,0Varias1005,20689.096.0Varias70.009:10
22402025-02-101212EASTURIAS AEROPUERTOASTURIAS12712,62,99,921:5715,212:296.02,87,814:401,21006,3001000,12491.097.0Varias77.012:30
22412025-02-111212EASTURIAS AEROPUERTOASTURIAS12713,23,88,807:0017,713:1427.03,612,522:006,61000,200994,81678.096.0Varias49.012:18

Duplicate rows

Most frequently occurring

fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachasolpresMaxhoraPresMaxpresMinhoraPresMinhrMediahrMaxhoraHrMaxhrMinhoraHrMin# duplicates
02019-04-011212EASTURIAS AEROPUERTOASTURIAS12710,20,05,703:1014,813:4521.01,96,704:1110,71002,1Varias998,30486.098.021:3062.009:242
12019-10-011212EASTURIAS AEROPUERTOASTURIAS12718,90,316,102:2121,709:1829.07,816,918:187,41003,1Varias997,30471.0100.0Varias53.023:292
22020-04-011212EASTURIAS AEROPUERTOASTURIAS1279,60,36,306:2012,914:2311.03,17,200:130,1999,7Varias994,90394.0100.0Varias76.010:222
32020-10-011212EASTURIAS AEROPUERTOASTURIAS12716,661,413,105:4620,014:2124.06,423,621:071,8997,8Varias979,42469.0100.0Varias50.014:372
42021-04-011212EASTURIAS AEROPUERTOASTURIAS12717,8Ip14,020:3421,603:0928.03,312,518:542,11000,2Varias993,10458.090.0Varias28.003:082
52021-10-011212EASTURIAS AEROPUERTOASTURIAS12718,41,813,405:2923,411:5426.01,98,914:593,51007,6001002,52481.096.0Varias54.012:112
62022-04-011212EASTURIAS AEROPUERTOASTURIAS1277,05,54,012:429,915:3199.02,59,701:175,11008,1Varias1002,50171.089.0Varias44.000:002
72022-10-011212EASTURIAS AEROPUERTOASTURIAS12717,20,012,906:0221,6Varias24.02,58,304:489,91011,4Varias1005,00276.090.0Varias59.009:482